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Record W2316891541 · doi:10.1093/gji/ggv547

A numerically exact local solver applied to salt boundary inversion in seismic full-waveform inversion

2016· article· en· W2316891541 on OpenAlexaff
Bram Willemsen, Alison Malcolm, Winston Lewis

Bibliographic record

VenueGeophysical Journal International · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsMemorial University of Newfoundland
FundersEidgenössische Technische Hochschule ZürichNational Science Foundation
KeywordsSolverInversion (geology)Boundary (topology)AlgorithmWaveformGeologyComputer scienceApplied mathematicsMathematical optimizationMathematical analysisMathematicsSeismology

Abstract

fetched live from OpenAlex

A R YIn a set of problems ranging from 4-D seismic to salt boundary estimation, updates to the velocity model often have a highly localized nature.Numerical techniques for these applications such as full-waveform inversion (FWI) require an estimate of the wavefield to compute the model updates.When dealing with localized problems, it is wasteful to compute these updates in the global domain, when we only need them in our region of interest.This paper introduces a local solver that generates forward and adjoint wavefields which are, to machine precision, identical to those generated by a full-domain solver evaluated within the region of interest.This means that the local solver computes all interactions between model updates within the region of interest and the inhomogeneities in the background model outside.Because no approximations are made in the calculation of the forward and adjoint wavefields, the local solver can compute the identical gradient in the region of interest as would be computed by the more expensive full-domain solver.In this paper, the local solver is used to efficiently generate the FWI gradient at the boundary of a salt body.This gradient is then used in a level set method to automatically update the salt boundary.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.217
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations48
Published2016
Admission routes1
Has abstractyes

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